{"product_id":"quantum-computing-models-for-cybersecurity-and-wireless-communications-hardback-9781394271399","title":"Quantum Computing Models for Cybersecurity and Wireless Communications (Hardback) 9781394271399","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eQuantum Computing Models for Cybersecurity and Wireless Communications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eBudati Anil Kumar (Edited by), SK Kumar (Author), Singamaneni Kranthi Kumar (Edited by), Li Xingwang (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394271399, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 14 May 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e25 x 15 x 1.5 cm, 0.68 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe book explores the latest quantum computing research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence-based devices, computer technology, and their solutions.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eFuture quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning. \u003c\/p\u003e\n\u003cp\u003eThis comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThis book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgment xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Performance Evaluation of Avionics System Under Hardware-In- Loop Simulation Framework with Implementation of an AS9100 Quality Management System 1\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRajesh Shankar Karvande and Tatineni Madhavi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 HILS Process and Quality Management System 4\u003c\/p\u003e \u003cp\u003e1.3 HILS Testing Phase 7\u003c\/p\u003e \u003cp\u003e1.4 AS9100 QMS Integrated with HILS Process 8\u003c\/p\u003e \u003cp\u003e1.5 Conclusion and Suggestions 10\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 YouTube Comment Summarizer and Time-Based Analysis 13\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePreeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 13\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 16\u003c\/p\u003e \u003cp\u003e2.3 Methodology 18\u003c\/p\u003e \u003cp\u003e2.3.1 YouTube Comments Data Collection 18\u003c\/p\u003e \u003cp\u003e2.3.1.1 YouTube Data API Integration 18\u003c\/p\u003e \u003cp\u003e2.3.1.2 get_video_comments Function 19\u003c\/p\u003e \u003cp\u003e2.3.1.3 Comment Processing 19\u003c\/p\u003e \u003cp\u003e2.3.1.4 Handling Pagination with get_all_video_ comments 20\u003c\/p\u003e \u003cp\u003e2.3.1.5 Excel File Creation with save_to_excel 20\u003c\/p\u003e \u003cp\u003e2.3.2 Datasets 20\u003c\/p\u003e \u003cp\u003e2.3.3 Extractive Summarization 21\u003c\/p\u003e \u003cp\u003e2.4 Result 30\u003c\/p\u003e \u003cp\u003e2.5 Performance 30\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 31\u003c\/p\u003e \u003cp\u003eReferences 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePremanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 34\u003c\/p\u003e \u003cp\u003e3.2 Related Works 34\u003c\/p\u003e \u003cp\u003e3.3 Methodology 36\u003c\/p\u003e \u003cp\u003e3.4 Comparision with Existing Systems 41\u003c\/p\u003e \u003cp\u003e3.5 Future Scope 48\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 48\u003c\/p\u003e \u003cp\u003eAcknowledgment 49\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction 51\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRamya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 52\u003c\/p\u003e \u003cp\u003e4.2 Related Works 53\u003c\/p\u003e \u003cp\u003e4.3 Proposed Framework 56\u003c\/p\u003e \u003cp\u003e4.3.1 ML Models and Ablation Study 56\u003c\/p\u003e \u003cp\u003e4.3.2 Building Ensemble Model Using AdaBoost 57\u003c\/p\u003e \u003cp\u003e4.4 Experimental Setup 58\u003c\/p\u003e \u003cp\u003e4.4.1 Dataset 58\u003c\/p\u003e \u003cp\u003e4.4.2 Data Visualization 59\u003c\/p\u003e \u003cp\u003e4.4.3 Data Pre-Processing Phase 60\u003c\/p\u003e \u003cp\u003e4.4.4 Proposed Methodology 61\u003c\/p\u003e \u003cp\u003e4.4.5 Performance Metrics 62\u003c\/p\u003e \u003cp\u003e4.5 Results and Discussion 63\u003c\/p\u003e \u003cp\u003e4.5.1 Comparison with Baseline Models 63\u003c\/p\u003e \u003cp\u003e4.5.2 Comparison with Existing Literature Works 66\u003c\/p\u003e \u003cp\u003e4.6 Existing Works 67\u003c\/p\u003e \u003cp\u003e4.7 Conclusion and Future Work 69\u003c\/p\u003e \u003cp\u003eDataset 69\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShelly Prakash and Vaibhav Vyas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 74\u003c\/p\u003e \u003cp\u003e5.1.1 Cloud Computing 75\u003c\/p\u003e \u003cp\u003e5.1.1.1 Fault, Error, Failure 75\u003c\/p\u003e \u003cp\u003e5.2 Related Work 77\u003c\/p\u003e \u003cp\u003e5.3 Proposed Proactive Fault Detection Architecture 81\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 95\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 FlowGuard: Efficient Traffic Monitoring System 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVarsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 99\u003c\/p\u003e \u003cp\u003e6.2 Literature Review 100\u003c\/p\u003e \u003cp\u003e6.3 Methodology 113\u003c\/p\u003e \u003cp\u003e6.3.1 Theory 113\u003c\/p\u003e \u003cp\u003e6.3.2 Requirement 114\u003c\/p\u003e \u003cp\u003e6.3.2.1 Hardware Requirements 114\u003c\/p\u003e \u003cp\u003e6.3.2.2 Software Requirements 116\u003c\/p\u003e \u003cp\u003e6.3.3 Workflow 117\u003c\/p\u003e \u003cp\u003e6.3.4 Flowchart 118\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussions 118\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 121\u003c\/p\u003e \u003cp\u003e6.6 Future Scope 121\u003c\/p\u003e \u003cp\u003eAcknowledgment 122\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003eReferences for Pictures of Components Used 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A Survey on Heart Disease Prediction Using Ensemble Techniques in ml 125\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSudhakar Vecha and M.V.P. Chandra Sekhara Rao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 125\u003c\/p\u003e \u003cp\u003e7.2 Literature Survey 127\u003c\/p\u003e \u003cp\u003e7.3 Datasets 128\u003c\/p\u003e \u003cp\u003e7.4 Ensemble Learning in Heart Disease 129\u003c\/p\u003e \u003cp\u003e7.5 Challenges and Limitations 134\u003c\/p\u003e \u003cp\u003e7.6 Future Directions 134\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 135\u003c\/p\u003e \u003cp\u003eReferences 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Video Surveillance: Crowd Anomaly Detection and Management Alert System 139\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 140\u003c\/p\u003e \u003cp\u003e8.2 Related Work 140\u003c\/p\u003e \u003cp\u003e8.3 Dataset Description 143\u003c\/p\u003e \u003cp\u003e8.4 Problem Definition 143\u003c\/p\u003e \u003cp\u003e8.5 Proposed Methodology and System 144\u003c\/p\u003e \u003cp\u003e8.5.1 Proposed Methodology 144\u003c\/p\u003e \u003cp\u003e8.5.2 Proposed System 146\u003c\/p\u003e \u003cp\u003e8.6 Results 148\u003c\/p\u003e \u003cp\u003e8.7 Conclusion and Future Scope 150\u003c\/p\u003e \u003cp\u003e8.7.1 Conclusion 150\u003c\/p\u003e \u003cp\u003e8.7.2 Future Scope 151\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 154\u003c\/p\u003e \u003cp\u003e9.1.1 Parallelism 154\u003c\/p\u003e \u003cp\u003e9.1.2 Quantum Speedup 155\u003c\/p\u003e \u003cp\u003e9.1.3 Quantum Entanglement 155\u003c\/p\u003e \u003cp\u003e9.1.4 Quantum Fourier Transform 155\u003c\/p\u003e \u003cp\u003e9.1.5 Quantum Machine Learning Algorithms 155\u003c\/p\u003e \u003cp\u003e9.1.6 Quantum Data Representation 155\u003c\/p\u003e \u003cp\u003e9.1.7 Quantum Sampling 155\u003c\/p\u003e \u003cp\u003e9.1.8 Quantum Annealing 156\u003c\/p\u003e \u003cp\u003e9.1.9 Hybrid Quantum-Classical Approaches 156\u003c\/p\u003e \u003cp\u003e9.2 Review of Literature 156\u003c\/p\u003e \u003cp\u003e9.2.1 Overview of Key Quantum Computing Principles 156\u003c\/p\u003e \u003cp\u003e9.2.1.1 Qubits (Quantum Bits) 157\u003c\/p\u003e \u003cp\u003e9.2.1.2 Quantum Gates 157\u003c\/p\u003e \u003cp\u003e9.2.1.3 Quantum Parallelism 157\u003c\/p\u003e \u003cp\u003e9.2.1.4 Quantum Measurement 157\u003c\/p\u003e \u003cp\u003e9.2.1.5 Quantum Fourier Transform 158\u003c\/p\u003e \u003cp\u003e9.2.1.6 Quantum Entanglement-Based Algorithms 158\u003c\/p\u003e \u003cp\u003e9.3 Basic Quantum Operations, Qubits, and Quantum Gates 158\u003c\/p\u003e \u003cp\u003e9.3.1 Basic Quantum Operations 158\u003c\/p\u003e \u003cp\u003e9.3.2 Quantum Bits (Qubits) 158\u003c\/p\u003e \u003cp\u003e9.3.3 Quantum Gates 159\u003c\/p\u003e \u003cp\u003e9.4 Quantum Machine Learning Algorithms 159\u003c\/p\u003e \u003cp\u003e9.4.1 Quantum Support Vector Machines (QSVM) 161\u003c\/p\u003e \u003cp\u003e9.4.2 Quantum Neural Networks (QNN) 161\u003c\/p\u003e \u003cp\u003e9.4.3 Quantum Clustering Algorithms 161\u003c\/p\u003e \u003cp\u003e9.4.4 Quantum Principal Component Analysis (QPCA) 162\u003c\/p\u003e \u003cp\u003e9.4.5 Quantum Boltzmann Machines 162\u003c\/p\u003e \u003cp\u003e9.4.6 Quantum Support Vector Clustering (QSVC) 162\u003c\/p\u003e \u003cp\u003e9.5 Quantum Hardware for Machine Learning 162\u003c\/p\u003e \u003cp\u003e9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware 163\u003c\/p\u003e \u003cp\u003e9.6.1 Decoherence and Quantum Error Correction 163\u003c\/p\u003e \u003cp\u003e9.6.2 Quantum Gate Fidelity 163\u003c\/p\u003e \u003cp\u003e9.6.3 Scalability 164\u003c\/p\u003e \u003cp\u003e9.6.4 Qubit Connectivity and Crosstalk 164\u003c\/p\u003e \u003cp\u003e9.6.5 Material Science and Qubit Implementation 164\u003c\/p\u003e \u003cp\u003e9.6.6 Quantum Interconnects 164\u003c\/p\u003e \u003cp\u003e9.6.7 Thermal Management 164\u003c\/p\u003e \u003cp\u003e9.6.8 Error Mitigation Strategies 164\u003c\/p\u003e \u003cp\u003e9.7 Challenges and Limitations in Quantum Machine Learning 165\u003c\/p\u003e \u003cp\u003e9.7.1 Quantum Computational Overheads 165\u003c\/p\u003e \u003cp\u003e9.7.2 Hybrid Quantum-Classical System Integration 165\u003c\/p\u003e \u003cp\u003e9.7.3 Limited Quantum Expressibility 165\u003c\/p\u003e \u003cp\u003e9.7.4 Data Preprocessing Challenges 165\u003c\/p\u003e \u003cp\u003e9.7.5 Quantum Algorithm Verification 166\u003c\/p\u003e \u003cp\u003e9.7.6 Quantum Resource Requirements 166\u003c\/p\u003e \u003cp\u003e9.7.7 Adaptation to Quantum Hardware Constraints 166\u003c\/p\u003e \u003cp\u003e9.7.8 Limited Quantum Hardware Availability 166\u003c\/p\u003e \u003cp\u003e9.7.9 Algorithmic Complexity 166\u003c\/p\u003e \u003cp\u003e9.7.10 Quantum Model Interpretability 166\u003c\/p\u003e \u003cp\u003e9.8 Future Directions 167\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 167\u003c\/p\u003e \u003cp\u003eReferences 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multi-Band Self-Grounding Antenna for Wireless Technologies 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCh. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 170\u003c\/p\u003e \u003cp\u003e10.1.1 Literature Review 170\u003c\/p\u003e \u003cp\u003e10.2 Design of Antenna 174\u003c\/p\u003e \u003cp\u003e10.2.1 Design and Results at Primary Level of Antenna 175\u003c\/p\u003e \u003cp\u003e10.2.2 Design and Results at Secondary Level of Antenna 175\u003c\/p\u003e \u003cp\u003e10.3 Actual Design of Antenna 176\u003c\/p\u003e \u003cp\u003e10.4 Results of Antenna 176\u003c\/p\u003e \u003cp\u003e10.4.1 Mathematical Analysis 178\u003c\/p\u003e \u003cp\u003e10.4.2 3D Polar Plot 178\u003c\/p\u003e \u003cp\u003e10.5 Conclusions 179\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies 183\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSai Ramya, Smera C. and Sandeep J.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 184\u003c\/p\u003e \u003cp\u003e11.2 Related Work 186\u003c\/p\u003e \u003cp\u003e11.3 Methodology 194\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 197\u003c\/p\u003e \u003cp\u003eReferences 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 File Fragment Classification: A Comprehensive Survey of Research Advances 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTeena Mary and Sreeja C.S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 201\u003c\/p\u003e \u003cp\u003e12.2 Methodology 203\u003c\/p\u003e \u003cp\u003e12.2.1 Selection Criteria 203\u003c\/p\u003e \u003cp\u003e12.2.2 Structure of the Paper 204\u003c\/p\u003e \u003cp\u003e12.3 Approaches for File Fragment Classification 204\u003c\/p\u003e \u003cp\u003e12.3.1 Signature-Based Approaches 204\u003c\/p\u003e \u003cp\u003e12.3.2 Content-Based Approaches 206\u003c\/p\u003e \u003cp\u003e12.3.3 Deep Learning-Based Approaches 207\u003c\/p\u003e \u003cp\u003e12.3.3.1 Convolutional Neural Networks (CNNs) 208\u003c\/p\u003e \u003cp\u003e12.3.3.2 Feed Forward Neural Networks (FFNNs) 209\u003c\/p\u003e \u003cp\u003e12.3.4 Hierarchical Classification Methods 209\u003c\/p\u003e \u003cp\u003e12.4 Survey Findings 210\u003c\/p\u003e \u003cp\u003e12.5 Challenges and Future Directions 214\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 215\u003c\/p\u003e \u003cp\u003eReferences 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Deepfake Detection and Forensic Precision for Online Harassment 219\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 220\u003c\/p\u003e \u003cp\u003e13.2 Literature 221\u003c\/p\u003e \u003cp\u003e13.3 Theoretical Analysis and Software Simulation 222\u003c\/p\u003e \u003cp\u003e13.3.1 Theoretical Analysis 222\u003c\/p\u003e \u003cp\u003e13.3.2 Software Simulation 223\u003c\/p\u003e \u003cp\u003e13.3.3 Testing and Optimization 224\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Design of Automatic Seed Sowing Machine 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 228\u003c\/p\u003e \u003cp\u003e14.2 Literature Survey 229\u003c\/p\u003e \u003cp\u003e14.3 Proposed System 232\u003c\/p\u003e \u003cp\u003e14.4 Conclusions 235\u003c\/p\u003e \u003cp\u003eReferences 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 In Motion: Exploring Urban Rides Through Data Analytics 237\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 237\u003c\/p\u003e \u003cp\u003e15.2 Literature Survey 238\u003c\/p\u003e \u003cp\u003e15.3 Proposed Methodology 240\u003c\/p\u003e \u003cp\u003e15.4 Result Analysis 247\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 248\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Design of Novel Chatbot Using Generative Artificial Intelligence 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 252\u003c\/p\u003e \u003cp\u003e16.2 Conclusion and Future Scope 257\u003c\/p\u003e \u003cp\u003eReferences 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 The Smart Nebulizer Cap for Enhanced Asthma Management 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRossly Netala, Aadi Praharsha and Mohan Dholvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 259\u003c\/p\u003e \u003cp\u003e17.2 Literature Survey 261\u003c\/p\u003e \u003cp\u003e17.3 Methodology 262\u003c\/p\u003e \u003cp\u003e17.4 Conclusions 265\u003c\/p\u003e \u003cp\u003eReferences 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 267\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eY. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 268\u003c\/p\u003e \u003cp\u003e18.2 Literature Survey 269\u003c\/p\u003e \u003cp\u003e18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 271\u003c\/p\u003e \u003cp\u003e18.4 Result Analysis 274\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 276\u003c\/p\u003e \u003cp\u003eReferences 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Intrusion Detection System Using Machine Learning 279\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBallikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 280\u003c\/p\u003e \u003cp\u003e19.2 Literature Survey 280\u003c\/p\u003e \u003cp\u003e19.3 Methodology 281\u003c\/p\u003e \u003cp\u003e19.4 Algorithm 283\u003c\/p\u003e \u003cp\u003e19.5 Implementation 285\u003c\/p\u003e \u003cp\u003e19.6 Results and Outputs 289\u003c\/p\u003e \u003cp\u003e19.6.1 User Interface 289\u003c\/p\u003e \u003cp\u003e19.7 Conclusion and Future Scope 290\u003c\/p\u003e \u003cp\u003eReferences 291\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Prediction of Arrival Delay Time in Freightage Rails 293\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 294\u003c\/p\u003e \u003cp\u003e20.2 Literature Survey 295\u003c\/p\u003e \u003cp\u003e20.3 Methodology 297\u003c\/p\u003e \u003cp\u003e20.4 Experimental Results 302\u003c\/p\u003e \u003cp\u003e20.5 Conclusions 308\u003c\/p\u003e \u003cp\u003eReferences 309\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers 311\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eL. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 311\u003c\/p\u003e \u003cp\u003e21.2 Literature Survey 312\u003c\/p\u003e \u003cp\u003e21.3 Proposed Methodology 314\u003c\/p\u003e \u003cp\u003e21.4 Result Analysis 322\u003c\/p\u003e \u003cp\u003e21.5 Conclusion 322\u003c\/p\u003e \u003cp\u003eReferences 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study 325\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVenkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 326\u003c\/p\u003e \u003cp\u003e22.2 Implementation of RCA \u0026amp; CSA 328\u003c\/p\u003e \u003cp\u003e22.3 Simulation Results 333\u003c\/p\u003e \u003cp\u003e22.4 Conclusion 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 XGBoost Classifier Based Water Quality Classification Using Machine Learning 337\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 338\u003c\/p\u003e \u003cp\u003e23.2 Related Work 338\u003c\/p\u003e \u003cp\u003e23.3 Proposed Methodology 339\u003c\/p\u003e \u003cp\u003e23.4 Results and Discussion 342\u003c\/p\u003e \u003cp\u003e23.5 Conclusion 345\u003c\/p\u003e \u003cp\u003eReferences 345\u003c\/p\u003e \u003cp\u003eIndex 347\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433242980632,"sku":"9781394271399","price":166.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394271399.jpg?v=1784852903","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/quantum-computing-models-for-cybersecurity-and-wireless-communications-hardback-9781394271399","provider":"Freshly Printed Books","version":"1.0","type":"link"}